Mapping the Horizons of Translational Ethics: an NLP analysis

Mapping the Horizons of Translational Ethics: an NLP analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mapping the Horizons of Translational Ethics: an NLP analysis Giovanni Spitale, Federico Germani, Kristine Baerøe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6990403/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The concept of “translation” is increasingly used to describe how ethical insights inform governance and policy-making. Yet, the diversity of translational pathways, the topics subject to translation, and the evolving role of ethics in the policy sphere remain underexplored. This study maps the biomedical literature on ethics-to-policy translation, analyzing 1,014 PubMed-indexed publications from 2014 to 2024. Using a natural language processing pipeline, we identify thematic trends and structural shifts in ethical governance. Our findings highlight a sharp rise in attention to artificial intelligence and digital technologies, with related terms showing the highest upward trends. At the same time, foundational ethical concepts such as autonomy, justice, and transparency, remain stable, underscoring their infrastructural role. We argue that translational ethics is expanding: broader in scope, more integrated across domains, and increasingly responsive to emergent regulatory challenges. This reflects a thickening interface between ethics and policy in a rapidly evolving biomedical landscape. Translational ethics ethics-to-policy bioethics ethical governance policy translation NLP natural language processing network analysis evidence synthesis Figures Figure 1 Figure 2 Introduction ‘Translation’ is being established as a popular term in bioethical literature. However, the term is used for describing dissimilar kinds of movements that are being played out when crossing different kinds of relationships. Broadly considered, the term is applicable when crossing a divide between i) different areas for thoughts on ethics, ii) different levels of action guiding statements on ethics, and iii) different areas for knowledge production in ethics. In i), the interpretative meaning of words and their constellations can be translated from one ‘language’ to another. In the most trivial sense, translation is merely about saying what is being articulated in one language, e.g. English, into another language, like Italian or Norwegian. In a less trivial sense, it is about transporting meaning within one language, from one way of expressing an issue into another using other words to express the same essence. For example, ethical issues can be conveyed in philosophical terms assuming a disciplinary training. Translation takes place when the same meaning is expressed in other words, free of the technical jargon and therefore understandable without requiring prior exposure to the academic tradition, specialized vocabulary, or conceptual frameworks of philosophy. As captured by ii), translation can also be a matter of interpreting and specifying ethical principles, recommendations, policies or guidelines into more specifying levels of requested actions. For example, making sense of Beauchamp and Childress’ four bioethical principles (1) in the context of healthcare involves contextualizing how these principles help articulate what is ethically at stake in a concrete situation and, in doing so, identify specific beliefs about which pieces of information are ethically relevant for action. Furthermore, even when guidelines broadly outline which circumstances warrant certain actions, ‘translation’ is still required – through association and reasoning – to subsume the specific situation at hand under the general categories described. “Translation” across different practices of knowledge production, as described in iii), can occur, for example, when theoretical conclusions about scientific misconduct drawn from research ethics are applied in the practical contexts of producing results in, e.g., clinical trials. Similarly, as noted by Kagarise and Sheldon, translational ethics emerge when contributions from research ethics codes are incorporated into the ethics of clinical practice (2). More fundamentally, “translation” can be understood here as bridging the gap between the production of theoretical ethical conclusions and the formulation of practical ones – those embedded in actual decision-making and action. Identifying this gap allows for further specification of what ‘translation’ in ethics can encompass. We can distinguish between ‘translation’ that goes from academia to the field of practice, and ‘translation’ that goes from the field of practice into academia (3). Moreover, we can distinguish between forms of ‘translation’ that transfer knowledge step-by-step across the theory–practice gap, whether through individual initiatives or well-designed implementation strategies (4,5). In addition to identifying various forms that ‘translation’ may take, it is worth mentioning that normative approaches to translation in ethics are emerging. Such approaches center on how ‘translation’ should be brought out in practical ethics research. A key challenge in ethics-to-policy translation is the theory–practice gap, where ethical analyses remain abstract and disconnected from implementation. Several normative approaches to translational ethics address this challenge by discussing ‘translation’ in terms of how to embed academic ethics into real-world policies and actions (6,7). Arguably, ethics research that aims to be practically relevant, should provide the foundation for policies that are not only practical but also normatively justifiable, ensuring they promote social dimensions of fairness, legitimacy, and accountability (4). There is also reason to emphasize the need to integrate self-reflexivity over one’s own research’s premises and assumptions when aiming to translating academic work on ethics into real-world practice. The task of ethics researchers is not only to consider how ethics informs policy, but also how policy realities shape the determinants of practical ethics research and, in turn, constrain the actual conditions for ethical inquiry (4,5,8). This approach also involves translating critical reflections on how some researchers are granted the power to investigate selected ethical issues, while others’ opportunities to raise their own concerns remain limited, into the design of projects that aim for ethically justifiable knowledge production in ethics (4). This typology of “translation in ethics” is not intended to be exhaustive. It aims, however, to serve as an analytical backdrop for different kinds of “translation” the literature may cover. The category of “translation” used for directing the mapping presented below is implicitly aligned with the third type of translation (iii) described above, i.e., translation across different areas of knowledge production in ethics. More specifically, it reflects a conception of translation as the movement of structured ethical ideas (such as principles, frameworks, or theories) from academic contexts into regulation-oriented outputs that are expected to inform or guide action. While being aware that this operationalization of “translation” does not aim to cover the full typology of translational movements in ethics between ideas and practical impact, it allows us to empirically explore what bioethical themes are discussed in ethics literature, in relation to implementation into policy and regulatory work, capturing the translation movements that are within academics’ control. This paper offers a high-level empirical overview of which topics, areas, and issues are discussed in the biomedical literature focused on how structured ethical ideas, such as principles, frameworks, or theories, are translated into policies, governance, and regulation. The search strategy does not draw a strict line between normative proposals, implementation efforts, and applied governance initiatives. As a result, the corpus includes both academic discussions of proposed ethical frameworks and analyses of their practical implementation and application in governance settings. Overall, this mapping provides information about which areas are getting academic attention regarding translation into potentially impactful, regulatory work. This is useful information that can direct academic work to mitigate attention biases and feed into translational cycles of fair use of academic efforts and funding Methods All data and code used in this study, unless otherwise specified, are openly available in the study's OSF repository (9) to ensure transparency and adherence to open science best practices. Additionally, the repository’s wiki documents the research processes in detail, including the original search queries and iterative refinements, providing a comprehensive account of the methodological steps taken in this study. Data collection To perform a frequency analysis and a network analysis of the literature on ethics-policy translation in the biomedical and health-related area, we collected PubMed records of papers on ethics to policy translation using smart iterative search strategies (10). We began with an initial, broad query aimed at capturing the intersection of ethics and policy translation into what are intended to be actionable governance frameworks – though we acknowledge that many such frameworks may lack the contextual grounding and implementation considerations necessary to be truly actionable in practice. This query (v0) included terms related to ethics, policy, and translation across publications from 2005 to 2025, yielding 4006 results from PubMed. We limited our search to PubMed as it provides a comprehensive and structured repository of biomedical and health-related literature, ensuring consistency in the scope and quality of indexed publications. PubMed’s indexing standards, metadata structure, and integration with MeSH terms allow for precise retrieval of relevant records, particularly in topics related to ethics, policy, and governance in healthcare. While other databases such as Scopus or Web of Science could offer additional coverage, our focus on PubMed ensures methodological consistency, avoids redundancy, and leverages established biomedical indexing conventions that align well with our study's objectives. To refine the search, we expanded the query by incorporating additional keywords mined via TopicTracker (11) from the initial set of records. In detail, we specified keywords referring to structured work in ethics such as “framework”, “principles”, or “theory”. We also expanded the keywords used to target the “translation to policy” concept, by including more keywords in the same semantic space of “translation”, such as “implementation” and “enforcement”, and by broadening the scope of policy-related terms to include e.g. “guidelines” and “regulations”. Additionally, in order to capture publications significant for the scope of this work, we specified the publication types to include “Evaluation Study”, “Validation Study”, “Observational Study”, “Review”, “Systematic Review”, “Meta-Analysis”, “Guideline”, “Practice Guideline”, “Consensus Development Conference”, “Government Publication”, “Technical Report”, and “Expression of Concern”. We also limited the temporal scope of the query to papers published from 2014 to 2024. Finally, in order to consistently apply the natural language processing pipelines without introducing bias due to automatic translations, we limited the results to English-language publications. This refined search strategy (v1) reduced the dataset to 1,014 relevant records. Data Processing Records from PubMed were downloaded using TopicTracker (11). Data representing the top 200 keywords in the dataset (i.e. the 200 keywords with the highest relative frequency) were then imported into Gephi (12), a network visualization and analysis tool used to explore and map relationships within complex datasets, for further network analysis. Frequency analysis We examined the evolution of keywords in the ethics-to-policy interface using their normalized frequencies. Normalized frequencies, calculated as the number of times a keyword appears divided by the total number of publications in a given year, allow us to account for variations in publication volume over time. Our analysis considers three key aspects of keyword dynamics: top keywords, trending keywords, and de-trending keywords. Top keywords represent the most frequently used terms across the entire time period, providing insight into foundational and consistently discussed concepts in the field. Trending keywords highlight those terms that have shown the greatest increase in prominence, revealing emerging areas of interest and shifts in scholarly focus. Conversely, de-trending keywords indicate terms whose prominence has declined most significantly, pointing to areas that may have become less central to current debates or integrated into broader frameworks. To quantify changes over time, we used a trend metric. This metric measures the difference between a keyword’s normalized frequency in 2024 (the most recent year analyzed) and its normalized frequency in 2014 (the earliest year analyzed). A positive trend value indicates that a keyword’s frequency has increased over time. A negative trend value indicates a decrease in frequency, reflecting a waning focus on that particular topic. A zero trend value signifies no net change in prominence over the ten-year period. Network Analysis In Gephi, we conducted a modularity class analysis (13) to identify clusters of keywords within the network. In this context, a modularity class is a feature of network analysis that groups nodes (i.e., keywords) into clusters based on their interconnectedness –– how frequently and strongly they co-occur with one another. These clusters represent thematic groupings within the literature, revealing related concepts and topics. The modularity class analysis was performed using a resolution parameter set to 0.5, which controls the granularity of the detected clusters. A lower resolution identifies larger, more generalized clusters, while a higher resolution identifies smaller, more specific ones. The resolution of 0.5 was selected empirically and based on previous work (11,14) to strike a balance between these extremes, ensuring that meaningful thematic groupings could emerge without losing important details. The presence in our results of a small number of free-floating keywords confirms the appropriateness of the resolution parameter we set. The analysis is based on normalized edge weights, which represent the strength of connections between nodes (i.e., the frequency or importance of keyword co-occurrences). Normalization is performed by dividing the co-occurrence count for each couple of keywords by the number of documents in the corpus that contain two or more keywords. This adjusts for variations in corpus size and ensures that edge weights reflect relative importance rather than raw frequency, enabling meaningful comparisons across edges. Finally, we employed randomization during the modularity calculation process, which helps the algorithm avoid local optima and improves the overall optimization of community detection (15,16). This combination of parameters and methods ensures robust identification of communities within the network, revealing the structural organization of the dataset. Network visualization We sized the nodes in the network based on their normalized frequency, which represents the relative importance of each keyword within the dataset. Node normalization ensures that the size of each node reflects its prominence across the entire corpus. To improve readability and maintain a consistent aesthetic, we visually rescaled node sizes to range between 10 and 40. We assigned colors to the nodes according to their modularity class. By assigning a unique color to each modularity class, we enhanced the visual differentiation between thematic clusters, making it easier to identify and interpret groups of related concepts within the network. Finally, the topology of the network was organized based on the modularity class, reflecting the natural structure of relationships among keywords. To create an initial arrangement, we used the Circle Pack layout, which positions nodes to emphasize their community affiliations. After generating the layout, we manually fine-tuned the positions of nodes to further enhance the readability and interpretability of clustered themes. Cluster thematic analysis We performed a manual thematic analysis of the clusters identified through the network analysis to gain deeper insights into the relationships between keywords and their broader thematic context. After identifying clusters based on modularity class, we examined the nodes within each cluster to interpret their conceptual connections and overarching themes. This process involved reviewing the most prominent keywords in each cluster, assessing their co-occurrence patterns, contextualizing them within the broader semantic space, and assessing the underlying literature. We retrieved papers referring to clusters by filtering our dataset of records with the keywords which compose the clusters, ordering the results by number of matches. This process ensures that the data-driven, algorithmically defined clusters are corroborated and informed with human-made manual interpretations of the underlying literature they represent. Results Results are based on the analysis of the dataset resulting from query v1, comprising 1014 records. Frequency analysis The analysis of normalized keyword frequencies across the 2014–2024 period highlights both enduring focal points and significant shifts in focus in the ethics-to-policy interface. Among the most frequently occurring keywords, “ethics” stands out with a total normalized frequency of 1.16 (a normalized frequency greater than 1 indicates that some articles have used “ethics” as a keyword multiple times). Following “ethics”, “artificial intelligence” emerges as the second most frequent keyword, with a total normalized frequency of 0.60, highlighting the growing attention to AI and its ethical implications. Other high-frequency keywords include “health policy” (0.31) and “public health” (0.27), reflecting the ongoing integration of ethical considerations into public health governance, as well as “policy” (0.26), a term that emphasizes the interface between ethics and governance. Normalized frequencies of the top 10 keywords in the dataset and their descriptive statistics are reported in Table 1. Table 1. Descriptive Statistics of the Top 10 Most Frequent Keywords in Ethics-to-Policy Literature (2014–2024). This table presents descriptive statistics for the top 10 most frequently occurring keywords in the ethics-to-policy literature, based on normalized frequency analysis. "Total" represents the cumulative normalized frequency across all years. "Min" and "Max" indicate the lowest and highest normalized yearly frequencies recorded for each keyword, while "Mean" represents the average normalized yearly frequency. "Std" denotes the standard deviation, reflecting the variability in normalized frequency over time. Keywords Total Min Std Mean Max ethics 1.16 0.02 0.05 0.10 0.21 artificial intelligence 0.60 0 0.06 0.05 0.18 health policy 0.31 0.02 0.02 0.03 0.07 public health 0.27 0 0.02 0.02 0.06 policy 0.26 0 0.02 0.02 0.06 research ethics 0.25 0 0.02 0.02 0.06 systematic review 0.24 0 0.02 0.02 0.07 machine learning 0.22 0 0.02 0.02 0.07 regulation 0.20 0 0.02 0.02 0.05 medical ethics 0.18 0 0.02 0.02 0.05 In terms of keywords that have increased the most in prominence, “artificial intelligence” exhibits the largest positive trend, with its normalized frequency rising from 0.000 in 2014 to 0.179 in 2024. This growth reflects its rapid emergence as a central topic in ethics-policy research. Similarly, “machine learning” (+0.053), “deep learning” (+0.044), and “chatgpt” (+0.040) show upward trajectories. “Ethics” showed an increase of +0.124, reflecting a substantial and sustained focus on its foundational importance within this research domain. “Public health” (+0.036), “review” (+0.031) and “scoping review” (+0.027) also feature among the most trending terms, suggesting respectively a sustained interest in applying ethical principles to public health challenges, and the role of reviews in evidence synthesis for policy translation. The analysis of keywords with the steepest negative trends reveals that the variations in normalized frequency are relatively small, suggesting that while certain topics have decreased in prominence, the changes are not drastic. This highlights a gradual shift in focus rather than a complete departure from these areas of research and discussion. The keyword “incidental findings” shows the largest decrease (-0.049); a set of keywords exhibit identical negative trends of -0.024: these include “qualitative research”, “distributive justice”, “cancer”, and “conflict of interest”, each representing areas where relative focus has declined. Overall, the decreases observed in these keywords point to evolving priorities in the ethics-to-policy interface. Although these areas might be receiving slightly less attention, their consistent presence in the dataset indicates that they still contribute to the broader conversation, albeit with a reduced focus. Trends are reported in Figure 1. Cluster themes We identified 12 main clusters, one smaller, highly specific cluster (cluster 13), and a few free-floating keywords. The clusters cover a broad spectrum of topics related to translating structured ethical work into policy and regulations, ranging from core ethics and public health to artificial intelligence, mobile health technologies, and genome editing. A detailed list of keywords composing in-cluster themes, and a granular characterization of said themes, is available as supplementary material via this study’s OSF repository (in the “wiki” section) (9). Clusters and their overall organization in a semantic network map are reported in Figure 2. Cluster 1: Core ethics This cluster represents how academic literature conceptualizes the complex relationship between ethics and governance in biomedical contexts, addressing both theoretical and practical aspects; it explores how structured ethical principles are integrated into regulatory frameworks to guide legislation and governance systems in healthcare. Clinical trials and translational research feature prominently, with a focus on ethical oversight to ensure patient safety, adherence to protocols, and the equitable application of emerging medical innovations such as regenerative and personalized medicine. Ethical considerations extend to preclinical research, emphasizing principles like the 3Rs (Replace, Reduce, Refine) to minimize harm in animal studies. The cluster also touches on communication and stakeholder interaction, exploring how autonomy and moral considerations influence decision-making processes in healthcare and research. Relevant literature representative of this cluster includes the ESPEN guideline on ethical aspects of artificial nutrition and hydration (17), Japan’s regulatory approach to stem cell-based interventions (18), and guidance on pediatric research ethics and regulation (19). Other examples cover the assessment of Clinical Ethics Consultations, aimed at upholding ethical, clinical, and legal standards (20), the German AnimalTestInfo database’s role in transparency and 3R strategies (21), and ethical guidance for donation physicians balancing end-of-life care with organ donation responsibilities (22). Cluster 2: AI This cluster, by far the largest after cluster 1 on core ethics, and the one growing faster, as inferable from the frequency analysis ( Figure 1 ), examines the integration of artificial intelligence into healthcare, focusing on its applications, ethical challenges, and societal implications. It highlights the transformative potential of technologies such as machine learning, deep learning, and natural language processing in fields like diagnostics, medical imaging, and robotics. Generative AI tools, including large language models like ChatGPT, are increasingly relevant for patient communication (23), medical education (24), and synthesizing data (25). However, ethical issues such as algorithmic bias, accountability, transparency, and trust (26) remain central to discussions about AI ethics. The cluster also explores how AI tools are reshaping medical and patient education, providing tailored learning experiences. Finally, the use of AI in medical devices and robotics signals advancements in precision medicine, though these innovations raise questions about equitable access and ethical oversight. Examples of literature representing this cluster include discussions on AI-driven oncological imaging, particularly for lung cancer, emphasizing clinical applications, adoption hurdles, and ethical concerns (27). Similarly, Reddy and colleagues propose a framework to assess AI systems’ real-world applicability, focusing on translational and ethical features (28). Addressing regulatory aspects, Pantanowitz and colleagues explore the need for flexible yet effective oversight to balance innovation, public trust, and compliance (29). More broadly, Kazim and Koshiyama provide a conceptual overview of AI ethics, tracing its foundations and highlighting key challenges in integrating ethical principles into AI-driven healthcare (30). Cluster 3: Ethics and public health during crises This cluster focuses on the role of ethical principles in guiding public health responses during crises such as pandemics and infectious disease outbreaks. Justice, beneficence, transparency, and societal acceptability emerge as foundational principles in managing public health challenges. The cluster examines key interventions like vaccination campaigns, preventive strategies, and screening programs, emphasizing the importance of evidence-based and ethically informed approaches. Crises such as COVID-19 reveal the strain on ethical frameworks due to the urgency of decisions, resource allocation challenges, and the need for public compliance (31). Long-term public health issues like HIV and addiction highlight persistent ethical dilemmas, such as disclosure, stigma and unequal access to care (32), highlighting the importance of justice and transparency in maintaining societal trust. Examples of literature representing this cluster include analyses of ethical challenges in public health interventions, such as the use of social media data for research; Takats and colleagues highlight gaps in ethical oversight, anonymization practices, and data validity in Twitter-based public health studies, emphasizing the need for standardized ethical frameworks (33). Ethical considerations also emerge in risk-stratified screening programs, where Hall and colleagues explore the balance between autonomy, justice, and state responsibility in implementing genotyping-based cancer screening (34). Saadi and colleagues address ethical and policy dilemmas in COVID-19 vaccine prioritization, finding that different strategies optimize different outcomes while revealing gaps in data for low- and middle-income countries (35). Beyond pandemic responses, long-term public health challenges are examined in a systematic review assessing nurses’ and midwives’ experiences with provider-initiated HIV testing, underscoring ethical tensions around resource allocation, patient autonomy, and structural barriers to care (36). Cluster 4: Ethical decision-making in public health This cluster, closely related to the previous one (cluster 3) both in terms of theme and co-occurrences, explores the integration of ethical considerations into public health policy and decision-making. It reflects on the role of governance systems in addressing societal challenges while ensuring ethical standards, with a focus on consent, equity, and justice. In this cluster, evidence-based methodologies such as health technology assessments and systematic reviews are highlighted as essential tools for evaluating interventions and translating ethical principles into effective policies. Specific applications, such as immunization, newborn screening, and palliative care, are considered alongside broader societal stressors like climate change. In general, this cluster illustrates how public health policy must navigate complex ethical landscapes to address diverse challenges in a fair and effective manner. Examples of literature representing this cluster include analyses of ethical challenges in public health policy and decision-making across various contexts. The MORECARE_C guidance addresses ethical complexities in end-of-life care research, providing evidence-based recommendations to ensure the ethical inclusion of individuals lacking capacity (37). Kharwadkar and colleagues highlight the impact of climate change on tuberculosis risk factors, underscoring the need for integrating environmental considerations into public health strategies (38). Issues of governance and transparency emerge in Papageorgiou and colleagues, which examines the ethical tensions surrounding the British NHS Digital–Home Office patient data-sharing agreement and its consequences for migrant healthcare access (39). More broadly, Behzadifar and colleagues review health policy analyses in the Eastern Mediterranean, emphasizing the gap between evidence-based research and policy implementation, reinforcing the need for stronger integration of ethical and systematic evaluation in decision-making (40). Cluster 5: Justice and equity in global and environmental health This cluster examines the intersection of global and environmental health challenges with ethical principles, focusing on justice and equity. It explores how resource allocation and priority setting can address health disparities and the social determinants of health. Ethical frameworks, including concepts such as health equity, social justice, environmental justice, and distributive justice, provide the foundation for developing equitable public health policies (41,42). The cluster also highlights methodological tools like integrative reviews, risk assessments, and implementation science, which support the translation of ethical principles into actionable interventions. Discussions emphasize the need to consider both societal and environmental factors when addressing global health challenges. Examples of literature representative of this cluster explore the ethical dimensions of global and environmental health challenges, particularly in resource allocation and justice. Ashcraft and colleagues highlight the role of implementation science in scaling evidence-based neighborhood and policy interventions to advance environmental justice, emphasizing evidence-based interventions to address structural and social determinants of health (43). Oehring and Gunasekera examine the ethical tensions (mostly situated in areas like equity, resource allocation, governance, and sustainability) in operationalizing the Leave No One Behind principle, advocating for context-sensitive, community-driven approaches to health equity (44). Addressing environmental distributive justice, Di Fonzo and colleagues review how industrial pollution disproportionately affects marginalized communities (particularly on the basis of ethnicity, socioeconomic status, and occupation), calling for standardized assessment models to inform policy (45). Kaur and colleagues analyze priority-setting in low- and middle-income countries, revealing a dominant focus on cost-effectiveness while ethical, legal, and political factors remain underrepresented in decision-making (46). Cluster 6: Mobile health and apps This cluster explores the integration of digital and mobile health technologies into healthcare delivery. Tools like telemedicine, eHealth, and mobile apps are transforming healthcare by improving accessibility, enhancing communication, and enabling remote patient management (47). The cluster focuses on their application in general primary care, and in addressing cognitive health challenges, particularly in patients with dementia or cognitive impairment. Gamification is highlighted as an innovative approach to engage patients and improve cognitive function. Practical applications of these technologies include early assessment and primary care, where mobile health solutions help bridge gaps in healthcare delivery and provide tailored interventions for underserved populations. Examples of literature representative of this cluster examine the evolving role of digital and mobile health technologies in healthcare delivery. Geddes and colleagues propose a framework for remote neurobehavioral assessments in patients with cognitive impairment, demonstrating the feasibility of telemedicine for cognitive and functional evaluations in the context of COVID-19, despite challenges in validation (48). Also in the pandemic context, Jonnagaddala and colleagues explore how digital health and telehealth transformed Australia's primary care response, identifying key enablers like digital literacy alongside barriers such as access and equity concerns (49). Addressing mental health, Spahl and colleagues review ethical considerations regarding gamified digital interventions for young people, emphasizing limited focus on social embeddedness, gaps in ethical frameworks, and the need for ongoing ethical integration in design and implementation (50). Cluster 7: Reviews as tools for policy translation This cluster emphasizes the importance of reviews, particularly scoping reviews, in bridging the gap between ethics and policy. Reviews serve as a structured approach for synthesizing evidence, identifying gaps, and informing ethical frameworks. They play a critical role in evaluating the effectiveness of health services and interventions, ensuring that policies align with ethical principles while being evidence-based (51,52). The cluster also discusses emerging health technologies, such as genetic testing and precision medicine, as areas where reviews can provide valuable insights for ethical governance. Examples of literature representative of this cluster include reviews of economic evaluation approaches for precision medicine, identifying key methodological and policy challenges (53). Germani and colleagues examine ethical considerations in infodemic management, emphasizing trust, transparency, and community engagement as essential for ethical public health communication (54). Addressing AI in healthcare, Brereton and colleagues explore the role of documentation in translating medical modeling software into clinical practice, identifying barriers such as bias and governance while advocating for standardized frameworks to support ethical and effective implementation (55). Cluster 8: Research ethics and oversight This cluster focuses on the ethical governance of research practices, highlighting the role of institutional review boards and research ethics committees in ensuring compliance with the best ethical practices in research. In this cluster, core ethical principles such as informed consent, privacy, confidentiality, and disclosure are central to discussions about safeguarding participant rights and maintaining trust. The cluster also includes reflections on ethical considerations in clinical trials, diagnosis, and emerging fields like epigenetics and pregnancy research. The cluster highlights the complexity of ethical oversight in diverse research contexts, highlighting the need for robust governance structures. Examples of literature representative of this cluster examine ethical governance challenges across diverse research contexts, emphasizing informed consent, privacy, and disclosure. Baker and colleagues explore ethical issues in translational tissue engineering research, highlighting concerns around data integrity, patient selection, and uncertainty in early clinical trials (56). Addressing digital health research, Nebeker and colleagues propose a bidirectional model for Alzheimer's studies, advocating for participant-driven consent and personalized access to study data (57). Roguljić and colleagues examine privacy risks in publishing identifiable patient photographs, revealing inconsistencies in consent practices and inadequate deidentification methods, underscoring the need for stricter governance (58). Cluster 9: Integrity, equity, and innovation in health research This cluster explores the interplay between research integrity, ethical innovation, and equity in health research. Transparency, data sharing, and open science are identified as critical elements in promoting trust and collaboration. The cluster examines applications in genomics, nanomedicine, and fertility preservation, emphasizing the need for ethical and legal frameworks, such as GDPR (General Data Protection Regulation), to guide research practices. Equity and stakeholder engagement are highlighted as essential for ensuring that health research aligns with societal values and addresses ethical challenges. Regional perspectives, including discussions about Africa and countries such as Ghana, add depth to the cluster by emphasizing context-specific issues. Examples of literature representative of this cluster include analyses of the challenges of implementing nanoparticle-based therapies for gynecological cancers in Africa, highlighting the need for investment, policy reforms, and international collaboration to ensure equitable access (59). Knoppers and colleagues explore ethical and legal complexities in open data sharing within the Human Cell Atlas (HCA) initiative, particularly regarding global privacy regulations while maintaining scientific transparency (60). Addressing broader concerns of scientific integrity, Kretser and colleagues outline best practices for upholding ethical research standards, advocating for open science, transparency, and reproducibility to foster trust and accountability (61). Cluster 10: Mental health and illness This cluster centers on terms associated with mental health and mental illness, with a focus on behavioral and motivational aspects, population-specific considerations, healthcare contexts, and implementation challenges. The connections between the terms suggest potential areas of discussion, including the role of exercise and motivation in behavior change, the mental health needs of children and adolescents, and barriers to implementation in primary healthcare and psychiatry. Examples of literature representative of this cluster include a systematic review on the effects of motivational interviewing and wearable fitness trackers on physical activity (62); a study on the relationship between physical activity and adolescent mental wellbeing (63), and a systematic review aimed at understanding how psychiatry ethics differs from forensic psychiatry ethics (64). Cluster 11: Ethics at the margins of life This cluster relates to ethical dilemmas that arise at the boundaries of life and death, including issues such as cardiopulmonary resuscitation, euthanasia, and end-of-life care within the domains of bioethics and neuroethics. The inclusion of nursing highlights the role healthcare professionals play in the context of morally complex situations, where medical, ethical, and legal considerations intersect. Literature reviews within this cluster suggest a reflective and analytical approach to these debates, consolidating discussions on autonomy, dignity, and the ethical frameworks that guide decision-making in critical moments. Literature representative of this cluster includes a narrative review of the nursing ethics literature on euthanasia (65); a narrative review on the conditions defining moral distress (66); and ethical and logistical considerations on the use of extracorporeal cardiopulmonary resuscitation in relation to organ donation (67). Cluster 12: Patient-centeredness, education, and innovation This cluster focuses on the central role of patient-centered care and education in biomedical research and healthcare practices. In this cluster, adherence to ethical principles and human rights is highlighted as foundational to patient-centered approaches. Education is framed as a key enabler for addressing emerging ethical challenges, particularly in the context of big data and technological innovation. The cluster also explores how tools like biobanks and big data contribute to advancements in research, raising questions about the balance between innovation and patient-centered ethical practices. Literature representative of this cluster includes a systematic literature review on the ethical challenges in humanitarian surgery aimed at developing the foundation for an ethics curriculum for surgeons involved in humanitarian missions (68); a review on biobanking education, emphasizing the need for specialized training programs, updated university curricula, and international collaboration (69); and a study on the impact of Flipped Classroom and gamification on motivation, autonomy, and self-regulation in learning about healthy habits (70). Cluster 13: Genome editing and CRISPR-Cas9 This small but significant cluster centers on genome editing technologies, particularly CRISPR/Cas9. It explores the ethical, regulatory, and societal implications of these technologies, highlighting the need for robust frameworks to guide their application in biomedical research and healthcare. Examples of literature belonging to this cluster include reflections on the transformative potential of CRISPR-Cas9 in genome editing while highlighting ethical, moral, and safety concerns, particularly regarding human germline modifications, calling for stringent regulations, global dialogue, and responsible governance (71); and on the potential of genome editing in agriculture to address climate change, sustainability, and food security while analyzing how regulatory uncertainty and consumer skepticism in the EU have slowed adoption, potentially impacting global acceptance and agricultural innovation (72). Discussion This study mapped the evolution of structured ethical considerations as they translate into policy-relevant discourse within biomedical and health-related literature. Results showed a consistent prominence and an increasing trend of terms relating to emerging phenomena, such as “artificial intelligence”; simultaneously, subtle declines in previously salient terms in this literature suggest a reframing of older debates within broader ethical conversations. In this sense, one thematic issue that stands out as receiving less attention is sustainability, which displays a small negative trend of -0.012 and a normalized frequency of 0.075. This is noteworthy as the concept of sustainability provides normative tools to response to urgent issues related to climate and environmental change. The reason could be that the impact of climate is not perceived as pressing, or that there is too little knowledge about the associations between climate changes and health challenges ( 73 , 74 ). Thematic clustering uncovered a rich ecology of topics, ranging from AI and crisis ethics to justice in global health, and from mobile health to genomic technologies. Notably, reviews and evidence synthesis emerged as methodological bridges between theory and implementation, signaling the emergence of an increasingly systematic ethics-to-policy interface. Building on these results, we argue that a broader pattern emerges: the ethics-to-policy interface is not merely a straightforward, transactional shift of normative content from theory into practice. Instead, it represents a dialectical process in which ethical discourse itself evolves in response to pressures and affordances of policy contexts. Moreover, novel and emerging factors, such as AI, or public health crises, occur as drivers of the demand for ethical oversight. This aligns with the broader trajectory of policy discussions, where issues such as AI regulation and algorithmic bias ( 75 – 78 ), or ethical infodemic management ( 54 , 79 ) have emerged as pressing concerns, have been discussed in literature, and have resulted in translational guidance work. Let us zoom in on the dramatic emergence and rise of AI ethics as a paradigmatic case: this growth is not surprising, given the rapid deployment of AI systems across health, governance, and public communication. Generative AI models, predictive diagnostics, and decision-support algorithms have created new ethical terrains, redefining traditional concerns about bias, accountability, and trust ( 26 ), while posing novel questions around potential harm and epistemic opacity. As regulatory bodies attempt to catch up, the policy discourse has leaned on ethics to anticipate harm and safeguard rights ( 80 , 81 ). This mirrors concerns raised in recent literature, where AI has been described both a risk vector and a useful tool ( 75 ). Beyond emerging trends, the presence of enduring ethical concepts (such as accountability in cluster 2; acceptability, beneficence and transparency in cluster 3; different models of justice in cluster 5; privacy, disclosure and confidentiality in cluster 8; self-determination theory in cluster 10; and patient-centered care and human rights in cluster 12) across clusters is not surprising. Rather than fading or being diluted by the emergence of new areas of inquiry, these concepts have become infrastructural: embedded in frameworks, protocols, and standards that guide translational governance. As discussed by Evans, bioethics has evolved from a deliberative discipline into a pragmatic orientation, capable of both normative critique and procedural articulation ( 82 ). The persistence of these core ethics signals not necessarily stasis, but maturity: their incorporation into governance logics may mark a form of successful translation. In sum, our findings indicate that ethics has transitioned from being a peripheral issue to becoming increasingly integral and formative in policy agendas and governance frameworks (such as in the case of recent WHO guidance on infodemic management ( 79 ), and UNESCO’s on AI ethics ( 76 )) – and is continually reshaped by emerging policy demands driven by novel real-world challenges. This also supports the claim that translation in ethics involves distinguishable “movements of translation” or distinct “building blocks” of justifiable actions to bridge academic theory and practice effectively ( 4 , 5 ). It remains imperative for the academic community to engage in a sustained and focused reflection on the meta-ethical conditions required to ensure that the translation of research into practice is conducted justly and equitably. Conclusion What the overall picture suggests is that the horizon of ethics in translation is expanding. “Expansion” here does not mean proliferation alone, but also thickening: ethics is increasingly seen as not only a tool to resolve dilemmas, but to scaffold entire infrastructures of regulation, communication, and legitimacy of governance. This thickening is evident in the emerging positive trends of new concepts, in the structure of the clusters, in the presence of ethical concepts and the integration of ethical reflection into evidence-based methodologies, as well as in the integration of evidence-based methodologies into ethical reflection. In light of this, ethics-to-policy translation emerges as a field of iterative negotiation of how we ought to live together with our technologies, institutions and decisions. Limitations A limitation of this study is the restriction of our search to PubMed, which, while offering a structured and comprehensive repository for biomedical and health-related literature, may exclude relevant discussions from broader policy, legal, and social science domains. Although databases like Scopus or Web of Science could provide additional coverage, focusing on PubMed ensures methodological consistency, avoids redundancy, and aligns with the study’s emphasis on structured ethical work within healthcare and biomedical governance. Second, while the natural language processing tools employed in this study offer powerful capabilities for analyzing large volumes of text, it is important to acknowledge the limitations of the approach. Our analysis focuses on the normalized frequencies of keywords and on their co-occurrence patterns, which provides a high-level, broad mapping of the field but is not suited for fine-grained or nuanced assessments of the underlying content. This method allows us to identify trends, emerging areas of interest, shifts in focus, and thematic clusters across a vast body of literature, but it does not capture the depth or context of individual studies or the specific ways in which keywords are used. As a result, the findings presented here should be interpreted as a macro-level perspective, offering an overview of thematic patterns and trends rather than a detailed examination of specific topics. Drawing inferences beyond this high-level mapping, unless explicitly supported by literature or further analysis, would be unwarranted. The reliance on keyword frequencies means that subtle variations in the framing, context, or interpretation of concepts are likely to be missed, and complex interrelations between themes may not be fully captured. Declarations Author Contribution GS: Conceptualization; Methodology; Software; Data curation; Formal analysis; Visualization; Writing – original draft; Project administration.FG: Methodology; Validation; Writing – review & editing.KB: Conceptualization; Theoretical framework; Supervision; Writing – review & editing.All authors have read and approved the final version of the manuscript. Acknowledgement GS wishes to thank Tiglia Panevinos, whose quiet presence and occasional commentary on fragmented governance structures reminded us that even in systems of disorder, there is elegance in observation. Her critical reflections helped sharpen our analysis of how ethical discourse interfaces with complex regulatory ecosystems. Funding details This work was partly supported by a UZH GRC Travel Grant. Disclosure statement The authors report there are no competing interests to declare. Data availability statement The data supporting the results and the code used for analyses presented in the paper can be found in this study’s OSF repository: https://osf.io/g68a5/ Ethics and Consent to Participate declarations Not applicable. References Beauchamp TL, Childress JF. Principles of biomedical ethics. 7th ed. New York: Oxford University Press; 2013. 459 p. Kagarise MJ, Sheldon GF. Translational Ethics: A Perspective for the New Millennium. Arch Surg. 2000 Jan 1;135(1):39–45. Metselaar S. Translational bioethics as a two-way street. Developing clinical ethics support instruments with and for healthcare practitioners. Bioethics. 2024;38(3):233–40. Bærøe K. Translational bioethics: Reflections on what it can be and how it should work. Bioethics. 2024;38(3):187–95. Bærøe K. Translational ethics: an analytical framework of translational movements between theory and practice and a sketch of a comprehensive approach. BMC Med Ethics. 2014 Sep 30;15(1):71. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6990403","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483000624,"identity":"c1d631e9-48a3-477d-81a6-6834d99c2570","order_by":0,"name":"Giovanni Spitale","email":"data:image/png;base64,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","orcid":"","institution":"University of Zurich","correspondingAuthor":true,"prefix":"","firstName":"Giovanni","middleName":"","lastName":"Spitale","suffix":""},{"id":483000625,"identity":"acf04dc7-dd83-4711-955a-5b8811cf300a","order_by":1,"name":"Federico Germani","email":"","orcid":"","institution":"University of Zurich","correspondingAuthor":false,"prefix":"","firstName":"Federico","middleName":"","lastName":"Germani","suffix":""},{"id":483000626,"identity":"b96e101e-6fae-46bd-a378-9860530884d1","order_by":2,"name":"Kristine Baerøe","email":"","orcid":"","institution":"University of Oslo","correspondingAuthor":false,"prefix":"","firstName":"Kristine","middleName":"","lastName":"Baerøe","suffix":""}],"badges":[],"createdAt":"2025-06-27 09:53:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6990403/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6990403/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86641606,"identity":"9a412c05-d8c7-4154-beaa-ffeef948a9a0","added_by":"auto","created_at":"2025-07-14 08:21:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2296061,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends in Keyword Frequencies Over Time in Ethics-Policy Interface Research. \u003c/strong\u003eThe top plot shows the \u003cstrong\u003emost increasing keywords\u003c/strong\u003e from 2014 to 2024, indicating emerging topics. The bottom plot displays the \u003cstrong\u003emost decreasing keywords\u003c/strong\u003e, highlighting topics that have seen declining discussion. Plots use a consistent y-axis scale for better comparability.\u003c/p\u003e","description":"","filename":"figure1keywordtrends.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6990403/v1/276a0136e100f979ad5ba1b9.jpg"},{"id":86641609,"identity":"a81e9be2-e722-4816-904c-e61465241ca7","added_by":"auto","created_at":"2025-07-14 08:21:06","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":8278322,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNetwork Visualization of Keyword Co-Occurrences in Ethics-to-Policy Literature (2014–2024). \u003c/strong\u003eThis figure presents a semantic network map of keyword co-occurrences in literature on the ethics-to-policy interface, illustrating the structure of thematic relationships. Nodes represent individual keywords, while edges denote co-occurrence relationships, with thicker edges indicating stronger associations. The network is topologically organized into 13 thematic clusters using modularity class. Cluster 1: Core ethics; Cluster 2: AI; Cluster 3: Ethics and public health during crises; Cluster 4: Ethical decision-making in public health; Cluster 5: Justice and equity in global and environmental health; Cluster 6: Mobile health and apps; Cluster 7: Reviews as tools for policy translation; Cluster 8: Research ethics and oversight; Cluster 9: Integrity, equity, and innovation in health research; Cluster 10: Mental health and illness; Cluster 11: Ethics at the margins of life; Cluster 12: Patient-centeredness, education, and innovation; Cluster 13: Genome editing and CRISPR. Additionally, a small set of free-floating keywords\u003cstrong\u003e \u003c/strong\u003eappear as isolated terms.\u003c/p\u003e","description":"","filename":"Figure2ethicstopolicysemanticnetworkmap.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6990403/v1/43e9780e1005c1ff6576d033.jpg"},{"id":89262961,"identity":"b9c71ced-829d-41a6-8c8f-8f213c0d3e0d","added_by":"auto","created_at":"2025-08-18 07:23:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11480060,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6990403/v1/4872cf27-faac-4b80-9e7c-7eee6b89a2ce.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mapping the Horizons of Translational Ethics: an NLP analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u0026lsquo;Translation\u0026rsquo; is being established as a popular term in bioethical literature. However, the term is used for describing dissimilar kinds of movements that are being played out when crossing different kinds of relationships. Broadly considered, the term is applicable when crossing a divide between i) different areas for thoughts on ethics, ii) different levels of action guiding statements on ethics, and iii) different areas for knowledge production in ethics. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn i), the interpretative meaning of words and their constellations can be translated from one \u0026lsquo;language\u0026rsquo; to another. In the most trivial sense, translation is merely about saying what is being articulated in one language, e.g. English, into another language, like Italian or Norwegian. In a less trivial sense, it is about transporting meaning \u003cem\u003ewithin\u003c/em\u003e one language, from one way of expressing an issue into another using other words to express the same essence. For example, ethical issues can be conveyed in philosophical terms assuming a disciplinary training. Translation takes place when the same meaning is expressed in other words, free of the technical jargon and therefore understandable without requiring prior exposure to the academic tradition, specialized vocabulary, or conceptual frameworks of philosophy.\u003c/p\u003e\n\u003cp\u003eAs captured by ii), translation can also be a matter of interpreting and specifying ethical principles, recommendations, policies or guidelines into more specifying levels of requested actions. For example, making sense of Beauchamp and Childress\u0026rsquo; four bioethical principles (1) in the context of healthcare involves contextualizing how these principles help articulate what is ethically at stake in a concrete situation and, in doing so, identify specific beliefs about which pieces of information are ethically relevant for action. Furthermore, even when guidelines broadly outline which circumstances warrant certain actions, \u0026lsquo;translation\u0026rsquo; is still required \u0026ndash; through association and reasoning \u0026ndash; to subsume the specific situation at hand under the general categories described.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;Translation\u0026rdquo; across different practices of knowledge production, as described in iii), can occur, for example, when theoretical conclusions about scientific misconduct drawn from research ethics are applied in the practical contexts of producing results in, e.g., clinical trials. Similarly, as noted by Kagarise and Sheldon, translational ethics emerge when contributions from research ethics codes are incorporated into the ethics of clinical practice (2). More fundamentally, \u0026ldquo;translation\u0026rdquo; can be understood here as bridging the gap between the production of theoretical ethical conclusions and the formulation of practical ones \u0026ndash; those embedded in actual decision-making and action. Identifying this gap allows for further specification of what \u0026lsquo;translation\u0026rsquo; in ethics can encompass. \u0026nbsp;We can distinguish between \u0026lsquo;translation\u0026rsquo; that goes from academia to the field of practice, and \u0026lsquo;translation\u0026rsquo; that goes from the field of practice into academia (3). Moreover, we can distinguish between forms of \u0026lsquo;translation\u0026rsquo; that transfer knowledge step-by-step across the theory\u0026ndash;practice gap, whether through individual initiatives or well-designed implementation strategies (4,5).\u003c/p\u003e\n\u003cp\u003eIn addition to identifying various forms that \u0026lsquo;translation\u0026rsquo; may take, it is worth mentioning that normative approaches to translation in ethics are emerging. Such approaches center on how \u0026lsquo;translation\u0026rsquo; should\u003cem\u003e\u0026nbsp;\u003c/em\u003ebe brought out in practical ethics research. A key challenge in ethics-to-policy translation is the theory\u0026ndash;practice gap, where ethical analyses remain abstract and disconnected from implementation. Several normative approaches to translational ethics address this challenge by discussing \u0026lsquo;translation\u0026rsquo; in terms of how to embed academic ethics into real-world policies and actions (6,7). Arguably, ethics research that aims to be practically relevant, should provide the foundation for policies that are not only practical but also normatively justifiable, ensuring they promote social dimensions of fairness, legitimacy, and accountability (4). There is also reason to emphasize the need to integrate self-reflexivity over one\u0026rsquo;s own research\u0026rsquo;s premises and assumptions when aiming to translating academic work on ethics into real-world practice. The task of ethics researchers is not only to consider how ethics informs policy, but also how policy realities shape the determinants of practical ethics research and, in turn, constrain the actual conditions for ethical inquiry (4,5,8). This approach also involves translating critical reflections on how some researchers are granted the power to investigate selected ethical issues, while others\u0026rsquo; opportunities to raise their own concerns remain limited, into the design of projects that aim for ethically justifiable knowledge production in ethics (4). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis typology of \u0026ldquo;translation in ethics\u0026rdquo; is not intended to be exhaustive. It aims, however, to serve as an analytical backdrop for different kinds of \u0026ldquo;translation\u0026rdquo; the literature may cover. The category of \u0026ldquo;translation\u0026rdquo; used for directing the mapping presented below is implicitly aligned with the third type of translation (iii) described above, i.e., translation across different areas of knowledge production in ethics. More specifically, it reflects a conception of translation as the movement of structured ethical ideas (such as principles, frameworks, or theories) from academic contexts into regulation-oriented outputs that are expected to inform or guide action. While being aware that this operationalization of \u0026ldquo;translation\u0026rdquo; does not aim to cover the full typology of translational movements in ethics between ideas and practical impact, it allows us to empirically explore what bioethical themes are discussed in ethics literature, in relation to implementation into policy and regulatory work, capturing the translation movements that are within academics\u0026rsquo; control.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis paper offers a high-level empirical overview of which topics, areas, and issues are discussed in the biomedical literature focused on how structured ethical ideas, such as principles, frameworks, or theories, are translated into policies, governance, and regulation. The search strategy does not draw a strict line between normative proposals, implementation efforts, and applied governance initiatives. As a result, the corpus includes both academic discussions of proposed ethical frameworks and analyses of their practical implementation and application in governance settings. Overall, this mapping provides information about which areas are getting academic attention regarding translation into potentially impactful, regulatory work. This is useful information that can direct academic work to mitigate attention biases and feed into translational cycles of fair use of academic efforts and funding\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eAll data and code used in this study, unless otherwise specified, are openly available in the study\u0026apos;s OSF repository (9) to ensure transparency and adherence to open science best practices. Additionally, the repository\u0026rsquo;s wiki documents the research processes in detail, including the original search queries and iterative refinements, providing a comprehensive account of the methodological steps taken in this study.\u003c/p\u003e\n\u003ch2\u003eData collection\u003c/h2\u003e\n\u003cp\u003eTo perform a frequency analysis and a network analysis of the literature on ethics-policy translation in the biomedical and health-related area, we collected PubMed records of papers on ethics to policy translation using smart iterative search strategies (10).\u003c/p\u003e\n\u003cp\u003eWe began with an initial, broad query aimed at capturing the intersection of ethics and policy translation into what are intended to be actionable governance frameworks \u0026ndash; though we acknowledge that many such frameworks may lack the contextual grounding and implementation considerations necessary to be truly actionable in practice. This query (v0) included terms related to ethics, policy, and translation across publications from 2005 to 2025, yielding 4006 results from PubMed. We limited our search to PubMed as it provides a comprehensive and structured repository of biomedical and health-related literature, ensuring consistency in the scope and quality of indexed publications. PubMed\u0026rsquo;s indexing standards, metadata structure, and integration with MeSH terms allow for precise retrieval of relevant records, particularly in topics related to ethics, policy, and governance in healthcare. While other databases such as Scopus or Web of Science could offer additional coverage, our focus on PubMed ensures methodological consistency, avoids redundancy, and leverages established biomedical indexing conventions that align well with our study\u0026apos;s objectives.\u003c/p\u003e\n\u003cp\u003eTo refine the search, we expanded the query by incorporating additional keywords mined via TopicTracker (11) from the initial set of records. In detail, we specified keywords referring to structured work in ethics such as \u0026ldquo;framework\u0026rdquo;, \u0026ldquo;principles\u0026rdquo;, or \u0026ldquo;theory\u0026rdquo;. We also expanded the keywords used to target the \u0026ldquo;translation to policy\u0026rdquo; concept, by including more keywords in the same semantic space of \u0026ldquo;translation\u0026rdquo;, such as \u0026ldquo;implementation\u0026rdquo; and \u0026ldquo;enforcement\u0026rdquo;, and by broadening the scope of policy-related terms to include e.g. \u0026ldquo;guidelines\u0026rdquo; and \u0026ldquo;regulations\u0026rdquo;. Additionally, in order to capture publications significant for the scope of this work, we specified the publication types to include \u0026ldquo;Evaluation Study\u0026rdquo;, \u0026ldquo;Validation Study\u0026rdquo;, \u0026ldquo;Observational Study\u0026rdquo;, \u0026ldquo;Review\u0026rdquo;, \u0026ldquo;Systematic Review\u0026rdquo;, \u0026ldquo;Meta-Analysis\u0026rdquo;, \u0026ldquo;Guideline\u0026rdquo;, \u0026ldquo;Practice Guideline\u0026rdquo;, \u0026ldquo;Consensus Development Conference\u0026rdquo;, \u0026ldquo;Government Publication\u0026rdquo;, \u0026ldquo;Technical Report\u0026rdquo;, and \u0026ldquo;Expression of Concern\u0026rdquo;. We also limited the temporal scope of the query to papers published from 2014 to 2024. Finally, in order to consistently apply the natural language processing pipelines without introducing bias due to automatic translations, we limited the results to English-language publications.\u003c/p\u003e\n\u003cp\u003eThis refined search strategy (v1) reduced the dataset to 1,014 relevant records.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eData Processing\u003c/h2\u003e\n\u003cp\u003eRecords from PubMed were downloaded using TopicTracker (11). Data representing the top 200 keywords in the dataset (i.e. the 200 keywords with the highest relative frequency) were then imported into Gephi (12), a network visualization and analysis tool used to explore and map relationships within complex datasets, for further network analysis.\u003c/p\u003e\n\u003ch2\u003eFrequency analysis\u003c/h2\u003e\n\u003cp\u003eWe examined the evolution of keywords in the ethics-to-policy interface using their normalized frequencies. Normalized frequencies, calculated as the number of times a keyword appears divided by the total number of publications in a given year, allow us to account for variations in publication volume over time. Our analysis considers three key aspects of keyword dynamics: top keywords, trending keywords, and de-trending keywords. Top keywords represent the most frequently used terms across the entire time period, providing insight into foundational and consistently discussed concepts in the field. Trending keywords highlight those terms that have shown the greatest increase in prominence, revealing emerging areas of interest and shifts in scholarly focus. Conversely, de-trending keywords indicate terms whose prominence has declined most significantly, pointing to areas that may have become less central to current debates or integrated into broader frameworks. To quantify changes over time, we used a trend metric. This metric measures the difference between a keyword\u0026rsquo;s normalized frequency in 2024 (the most recent year analyzed) and its normalized frequency in 2014 (the earliest year analyzed). A positive trend value indicates that a keyword\u0026rsquo;s frequency has increased over time. A negative trend value indicates a decrease in frequency, reflecting a waning focus on that particular topic. A zero trend value signifies no net change in prominence over the ten-year period.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eNetwork Analysis\u003c/h2\u003e\n\u003cp\u003eIn Gephi, we conducted a modularity class analysis (13) to identify clusters of keywords within the network. In this context, a modularity class is a feature of network analysis that groups nodes (i.e., keywords) into clusters based on their interconnectedness \u0026ndash;\u0026ndash; how frequently and strongly they co-occur with one another. These clusters represent thematic groupings within the literature, revealing related concepts and topics. The modularity class analysis was performed using a resolution parameter set to 0.5, which controls the granularity of the detected clusters. A lower resolution identifies larger, more generalized clusters, while a higher resolution identifies smaller, more specific ones. The resolution of 0.5 was selected empirically and based on previous work (11,14) to strike a balance between these extremes, ensuring that meaningful thematic groupings could emerge without losing important details. The presence in our results of a small number of free-floating keywords confirms the appropriateness of the resolution parameter we set. The analysis is based on normalized edge weights, which represent the strength of connections between nodes (i.e., the frequency or importance of keyword co-occurrences). Normalization is performed by dividing the co-occurrence count for each couple of keywords by the number of documents in the corpus that contain two or more keywords. This adjusts for variations in corpus size and ensures that edge weights reflect relative importance rather than raw frequency, enabling meaningful comparisons across edges. Finally, we employed randomization during the modularity calculation process, which helps the algorithm avoid local optima and improves the overall optimization of community detection (15,16). This combination of parameters and methods ensures robust identification of communities within the network, revealing the structural organization of the dataset.\u003c/p\u003e\n\u003ch2\u003eNetwork visualization\u003c/h2\u003e\n\u003cp\u003eWe sized the nodes in the network based on their normalized frequency, which represents the relative importance of each keyword within the dataset. Node normalization ensures that the size of each node reflects its prominence across the entire corpus. To improve readability and maintain a consistent aesthetic, we visually rescaled node sizes to range between 10 and 40. We assigned colors to the nodes according to their modularity class. By assigning a unique color to each modularity class, we enhanced the visual differentiation between thematic clusters, making it easier to identify and interpret groups of related concepts within the network. Finally, the topology of the network was organized based on the modularity class, reflecting the natural structure of relationships among keywords. To create an initial arrangement, we used the Circle Pack layout, which positions nodes to emphasize their community affiliations. After generating the layout, we manually fine-tuned the positions of nodes to further enhance the readability and interpretability of clustered themes.\u003c/p\u003e\n\u003ch2\u003eCluster thematic analysis\u003c/h2\u003e\n\u003cp\u003eWe performed a manual thematic analysis of the clusters identified through the network analysis to gain deeper insights into the relationships between keywords and their broader thematic context. After identifying clusters based on modularity class, we examined the nodes within each cluster to interpret their conceptual connections and overarching themes. This process involved reviewing the most prominent keywords in each cluster, assessing their co-occurrence patterns, contextualizing them within the broader semantic space, and assessing the underlying literature. We retrieved papers referring to clusters by filtering our dataset of records with the keywords which compose the clusters, ordering the results by number of matches. This process ensures that the data-driven, algorithmically defined clusters are corroborated and informed with human-made manual interpretations of the underlying literature they represent.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eResults are based on the analysis of the dataset resulting from query v1, comprising 1014 records.\u003c/p\u003e\n\u003ch2\u003eFrequency analysis\u003c/h2\u003e\n\u003cp\u003eThe analysis of normalized keyword frequencies across the 2014\u0026ndash;2024 period highlights both enduring focal points and significant shifts in focus in the ethics-to-policy interface.\u003c/p\u003e\n\u003cp\u003eAmong the most frequently occurring keywords, \u0026ldquo;ethics\u0026rdquo; stands out with a total normalized frequency of 1.16 (a normalized frequency greater than 1 indicates that some articles have used \u0026ldquo;ethics\u0026rdquo; as a keyword multiple times). Following \u0026ldquo;ethics\u0026rdquo;, \u0026ldquo;artificial intelligence\u0026rdquo; emerges as the second most frequent keyword, with a total normalized frequency of 0.60, highlighting the growing attention to AI and its ethical implications. Other high-frequency keywords include \u0026ldquo;health policy\u0026rdquo; (0.31) and \u0026ldquo;public health\u0026rdquo; (0.27), reflecting the ongoing integration of ethical considerations into public health governance, as well as \u0026ldquo;policy\u0026rdquo; (0.26), a term that emphasizes the interface between ethics and governance. Normalized frequencies of the top 10 keywords in the dataset and their descriptive statistics are reported in \u003cstrong\u003eTable 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1. \u003cstrong\u003eDescriptive Statistics of the Top 10 Most Frequent Keywords in Ethics-to-Policy Literature (2014\u0026ndash;2024).\u0026nbsp;\u003c/strong\u003eThis table presents descriptive statistics for the top 10 most frequently occurring keywords in the ethics-to-policy literature, based on normalized frequency analysis. \u0026quot;Total\u0026quot; represents the cumulative normalized frequency across all years. \u0026quot;Min\u0026quot; and \u0026quot;Max\u0026quot; indicate the lowest and highest normalized yearly frequencies recorded for each keyword, while \u0026quot;Mean\u0026quot; represents the average normalized yearly frequency. \u0026quot;Std\u0026quot; denotes the standard deviation, reflecting the variability in normalized frequency over time.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"472\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cem\u003eKeywords\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cem\u003eMin\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cem\u003eStd\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cem\u003eMax\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cem\u003eethics\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cem\u003eartificial intelligence\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cem\u003ehealth policy\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cem\u003epublic health\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cem\u003epolicy\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cem\u003eresearch ethics\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cem\u003esystematic review\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cem\u003emachine learning\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cem\u003eregulation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cem\u003emedical ethics\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn terms of keywords that have increased the most in prominence, \u0026ldquo;artificial intelligence\u0026rdquo; exhibits the largest positive trend, with its normalized frequency rising from 0.000 in 2014 to 0.179 in 2024. This growth reflects its rapid emergence as a central topic in ethics-policy research. Similarly, \u0026ldquo;machine learning\u0026rdquo; (+0.053), \u0026ldquo;deep learning\u0026rdquo; (+0.044), and \u0026ldquo;chatgpt\u0026rdquo; (+0.040) show upward trajectories. \u0026ldquo;Ethics\u0026rdquo; showed an increase of +0.124, reflecting a substantial and sustained focus on its foundational importance within this research domain. \u0026ldquo;Public health\u0026rdquo; (+0.036), \u0026ldquo;review\u0026rdquo; (+0.031) and \u0026ldquo;scoping review\u0026rdquo; (+0.027) also feature among the most trending terms, suggesting respectively a sustained interest in applying ethical principles to public health challenges, and the role of reviews in evidence synthesis for policy translation. The analysis of keywords with the steepest negative trends reveals that the variations in normalized frequency are relatively small, suggesting that while certain topics have decreased in prominence, the changes are not drastic. This highlights a gradual shift in focus rather than a complete departure from these areas of research and discussion. The keyword \u0026ldquo;incidental findings\u0026rdquo; shows the largest decrease (-0.049); a set of keywords exhibit identical negative trends of -0.024: these include \u0026ldquo;qualitative research\u0026rdquo;, \u0026ldquo;distributive justice\u0026rdquo;, \u0026ldquo;cancer\u0026rdquo;, and \u0026ldquo;conflict of interest\u0026rdquo;, each representing areas where relative focus has declined. Overall, the decreases observed in these keywords point to evolving priorities in the ethics-to-policy interface. Although these areas might be receiving slightly less attention, their consistent presence in the dataset indicates that they still contribute to the broader conversation, albeit with a reduced focus. Trends are reported in \u003cstrong\u003eFigure 1.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eCluster themes\u003c/h2\u003e\n\u003cp\u003eWe identified 12 main clusters, one smaller, highly specific cluster (cluster 13), and a few free-floating keywords. The clusters cover a broad spectrum of topics related to translating structured ethical work into policy and regulations, ranging from core ethics and public health to artificial intelligence, mobile health technologies, and genome editing. A detailed list of keywords composing in-cluster themes, and a granular characterization of said themes, is available as supplementary material via this study\u0026rsquo;s OSF repository (in the \u0026ldquo;wiki\u0026rdquo; section) (9). Clusters and their overall organization in a semantic network map are reported in \u003cstrong\u003eFigure 2.\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003eCluster 1: Core ethics\u003c/h3\u003e\n\u003cp\u003eThis cluster represents how academic literature conceptualizes the complex relationship between ethics and governance in biomedical contexts, addressing both theoretical and practical aspects; it explores how structured ethical principles are integrated into regulatory frameworks to guide legislation and governance systems in healthcare. Clinical trials and translational research feature prominently, with a focus on ethical oversight to ensure patient safety, adherence to protocols, and the equitable application of emerging medical innovations such as regenerative and personalized medicine. Ethical considerations extend to preclinical research, emphasizing principles like the 3Rs (Replace, Reduce, Refine) to minimize harm in animal studies. The cluster also touches on communication and stakeholder interaction, exploring how autonomy and moral considerations influence decision-making processes in healthcare and research. Relevant literature representative of this cluster includes the ESPEN guideline on ethical aspects of artificial nutrition and hydration (17), Japan\u0026rsquo;s regulatory approach to stem cell-based interventions (18), and guidance on pediatric research ethics and regulation (19). Other examples cover the assessment of Clinical Ethics Consultations, aimed at upholding ethical, clinical, and legal standards (20), the German AnimalTestInfo database\u0026rsquo;s role in transparency and 3R strategies (21), and ethical guidance for donation physicians balancing end-of-life care with organ donation responsibilities (22).\u003c/p\u003e\n\u003ch3\u003eCluster 2: AI\u003c/h3\u003e\n\u003cp\u003eThis cluster, by far the largest after cluster 1 on core ethics, and the one growing faster, as inferable from the frequency analysis (\u003cstrong\u003eFigure 1\u003c/strong\u003e), examines the integration of artificial intelligence into healthcare, focusing on its applications, ethical challenges, and societal implications. It highlights the transformative potential of technologies such as machine learning, deep learning, and natural language processing in fields like diagnostics, medical imaging, and robotics. Generative AI tools, including large language models like ChatGPT, are increasingly relevant for patient communication (23), medical education (24), and synthesizing data (25). However, ethical issues such as algorithmic bias, accountability, transparency, and trust (26) remain central to discussions about AI ethics. The cluster also explores how AI tools are reshaping medical and patient education, providing tailored learning experiences. Finally, the use of AI in medical devices and robotics signals advancements in precision medicine, though these innovations raise questions about equitable access and ethical oversight. Examples of literature representing this cluster include discussions on AI-driven oncological imaging, particularly for lung cancer, emphasizing clinical applications, adoption hurdles, and ethical concerns \u0026nbsp;(27). Similarly, Reddy and colleagues propose a framework to assess AI systems\u0026rsquo; real-world applicability, focusing on translational and ethical features (28). Addressing regulatory aspects, Pantanowitz and colleagues explore the need for flexible yet effective oversight to balance innovation, public trust, and compliance (29). More broadly, Kazim and Koshiyama provide a conceptual overview of AI ethics, tracing its foundations and highlighting key challenges in integrating ethical principles into AI-driven healthcare (30).\u003c/p\u003e\n\u003ch3\u003eCluster 3: Ethics and public health during crises\u003c/h3\u003e\n\u003cp\u003eThis cluster focuses on the role of ethical principles in guiding public health responses during crises such as pandemics and infectious disease outbreaks. Justice, beneficence, transparency, and societal acceptability emerge as foundational principles in managing public health challenges. The cluster examines key interventions like vaccination campaigns, preventive strategies, and screening programs, emphasizing the importance of evidence-based and ethically informed approaches. Crises such as COVID-19 reveal the strain on ethical frameworks due to the urgency of decisions, resource allocation challenges, and the need for public compliance (31). Long-term public health issues like HIV and addiction highlight persistent ethical dilemmas, such as disclosure, stigma and unequal access to care (32), highlighting the importance of justice and transparency in maintaining societal trust. Examples of literature representing this cluster include analyses of ethical challenges in public health interventions, such as the use of social media data for research; Takats and colleagues highlight gaps in ethical oversight, anonymization practices, and data validity in Twitter-based public health studies, emphasizing the need for standardized ethical frameworks (33). Ethical considerations also emerge in risk-stratified screening programs, where Hall and colleagues explore the balance between autonomy, justice, and state responsibility in implementing genotyping-based cancer screening (34). Saadi and colleagues address ethical and policy dilemmas in COVID-19 vaccine prioritization, finding that different strategies optimize different outcomes while revealing gaps in data for low- and middle-income countries (35). Beyond pandemic responses, long-term public health challenges are examined in a systematic review assessing nurses\u0026rsquo; and midwives\u0026rsquo; experiences with provider-initiated HIV testing, underscoring ethical tensions around resource allocation, patient autonomy, and structural barriers to care (36).\u003c/p\u003e\n\u003ch3\u003eCluster 4: Ethical decision-making in public health\u003c/h3\u003e\n\u003cp\u003eThis cluster, closely related to the previous one (cluster 3) both in terms of theme and co-occurrences, explores the integration of ethical considerations into public health policy and decision-making. It reflects on the role of governance systems in addressing societal challenges while ensuring ethical standards, with a focus on consent, equity, and justice. In this cluster, evidence-based methodologies such as health technology assessments and systematic reviews are highlighted as essential tools for evaluating interventions and translating ethical principles into effective policies. Specific applications, such as immunization, newborn screening, and palliative care, are considered alongside broader societal stressors like climate change. In general, this cluster illustrates how public health policy must navigate complex ethical landscapes to address diverse challenges in a fair and effective manner. Examples of literature representing this cluster include analyses of ethical challenges in public health policy and decision-making across various contexts. The MORECARE_C guidance addresses ethical complexities in end-of-life care research, providing evidence-based recommendations to ensure the ethical inclusion of individuals lacking capacity (37). Kharwadkar and colleagues highlight the impact of climate change on tuberculosis risk factors, underscoring the need for integrating environmental considerations into public health strategies (38). Issues of governance and transparency emerge in Papageorgiou and colleagues, which examines the ethical tensions surrounding the British NHS Digital\u0026ndash;Home Office patient data-sharing agreement and its consequences for migrant healthcare access (39). More broadly, Behzadifar and colleagues review health policy analyses in the Eastern Mediterranean, emphasizing the gap between evidence-based research and policy implementation, reinforcing the need for stronger integration of ethical and systematic evaluation in decision-making (40).\u003c/p\u003e\n\u003ch3\u003eCluster 5: Justice and equity in global and environmental health\u003c/h3\u003e\n\u003cp\u003eThis cluster examines the intersection of global and environmental health challenges with ethical principles, focusing on justice and equity. It explores how resource allocation and priority setting can address health disparities and the social determinants of health. Ethical frameworks, including concepts such as health equity, social justice, environmental justice, and distributive justice, provide the foundation for developing equitable public health policies (41,42). The cluster also highlights methodological tools like integrative reviews, risk assessments, and implementation science, which support the translation of ethical principles into actionable interventions. Discussions emphasize the need to consider both societal and environmental factors when addressing global health challenges. Examples of literature representative of this cluster explore the ethical dimensions of global and environmental health challenges, particularly in resource allocation and justice. Ashcraft and colleagues highlight the role of implementation science in scaling evidence-based neighborhood and policy interventions to advance environmental justice, emphasizing evidence-based interventions to address structural and social determinants of health (43). Oehring and Gunasekera examine the ethical tensions (mostly situated in areas like equity, resource allocation, governance, and sustainability) in operationalizing the Leave No One Behind principle, advocating for context-sensitive, community-driven approaches to health equity (44). Addressing environmental distributive justice, Di Fonzo and colleagues review how industrial pollution disproportionately affects marginalized communities (particularly on the basis of ethnicity, socioeconomic status, and occupation), calling for standardized assessment models to inform policy (45). Kaur and colleagues analyze priority-setting in low- and middle-income countries, revealing a dominant focus on cost-effectiveness while ethical, legal, and political factors remain underrepresented in decision-making (46).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eCluster 6: Mobile health and apps\u003c/h3\u003e\n\u003cp\u003eThis cluster explores the integration of digital and mobile health technologies into healthcare delivery. Tools like telemedicine, eHealth, and mobile apps are transforming healthcare by improving accessibility, enhancing communication, and enabling remote patient management (47). The cluster focuses on their application in general primary care, and in addressing cognitive health challenges, particularly in patients with dementia or cognitive impairment. Gamification is highlighted as an innovative approach to engage patients and improve cognitive function. Practical applications of these technologies include early assessment and primary care, where mobile health solutions help bridge gaps in healthcare delivery and provide tailored interventions for underserved populations. Examples of literature representative of this cluster examine the evolving role of digital and mobile health technologies in healthcare delivery. Geddes and colleagues propose a framework for remote neurobehavioral assessments in patients with cognitive impairment, demonstrating the feasibility of telemedicine for cognitive and functional evaluations in the context of COVID-19, despite challenges in validation (48). Also in the pandemic context, Jonnagaddala and colleagues explore how digital health and telehealth transformed Australia\u0026apos;s primary care response, identifying key enablers like digital literacy alongside barriers such as access and equity concerns (49). Addressing mental health, Spahl and colleagues review ethical considerations regarding gamified digital interventions for young people, emphasizing limited focus on social embeddedness, gaps in ethical frameworks, and the need for ongoing ethical integration in design and implementation (50).\u003c/p\u003e\n\u003ch3\u003eCluster 7: Reviews as tools for policy translation\u003c/h3\u003e\n\u003cp\u003eThis cluster emphasizes the importance of reviews, particularly scoping reviews, in bridging the gap between ethics and policy. Reviews serve as a structured approach for synthesizing evidence, identifying gaps, and informing ethical frameworks. They play a critical role in evaluating the effectiveness of health services and interventions, ensuring that policies align with ethical principles while being evidence-based (51,52). The cluster also discusses emerging health technologies, such as genetic testing and precision medicine, as areas where reviews can provide valuable insights for ethical governance. Examples of literature representative of this cluster include reviews of economic evaluation approaches for precision medicine, identifying key methodological and policy challenges (53). Germani and colleagues examine ethical considerations in infodemic management, emphasizing trust, transparency, and community engagement as essential for ethical public health communication (54). Addressing AI in healthcare, Brereton and colleagues explore the role of documentation in translating medical modeling software into clinical practice, identifying barriers such as bias and governance while advocating for standardized frameworks to support ethical and effective implementation (55).\u003c/p\u003e\n\u003ch3\u003eCluster 8: Research ethics and oversight\u003c/h3\u003e\n\u003cp\u003eThis cluster focuses on the ethical governance of research practices, highlighting the role of institutional review boards and research ethics committees in ensuring compliance with the best ethical practices in research. In this cluster, core ethical principles such as informed consent, privacy, confidentiality, and disclosure are central to discussions about safeguarding participant rights and maintaining trust. The cluster also includes reflections on ethical considerations in clinical trials, diagnosis, and emerging fields like epigenetics and pregnancy research. The cluster highlights the complexity of ethical oversight in diverse research contexts, highlighting the need for robust governance structures. Examples of literature representative of this cluster examine ethical governance challenges across diverse research contexts, emphasizing informed consent, privacy, and disclosure. Baker and colleagues explore ethical issues in translational tissue engineering research, highlighting concerns around data integrity, patient selection, and uncertainty in early clinical trials (56). Addressing digital health research, Nebeker and colleagues propose a bidirectional model for Alzheimer\u0026apos;s studies, advocating for participant-driven consent and personalized access to study data (57). Roguljić and colleagues examine privacy risks in publishing identifiable patient photographs, revealing inconsistencies in consent practices and inadequate deidentification methods, underscoring the need for stricter governance (58).\u003c/p\u003e\n\u003ch3\u003eCluster 9: Integrity, equity, and innovation in health research\u003c/h3\u003e\n\u003cp\u003eThis cluster explores the interplay between research integrity, ethical innovation, and equity in health research. Transparency, data sharing, and open science are identified as critical elements in promoting trust and collaboration. The cluster examines applications in genomics, nanomedicine, and fertility preservation, emphasizing the need for ethical and legal frameworks, such as GDPR (General Data Protection Regulation), to guide research practices. Equity and stakeholder engagement are highlighted as essential for ensuring that health research aligns with societal values and addresses ethical challenges. Regional perspectives, including discussions about Africa and countries such as Ghana, add depth to the cluster by emphasizing context-specific issues. Examples of literature representative of this cluster include analyses of the challenges of implementing nanoparticle-based therapies for gynecological cancers in Africa, highlighting the need for investment, policy reforms, and international collaboration to ensure equitable access (59). Knoppers and colleagues explore ethical and legal complexities in open data sharing within the Human Cell Atlas (HCA) initiative, particularly regarding global privacy regulations while maintaining scientific transparency (60). Addressing broader concerns of scientific integrity, Kretser and colleagues outline best practices for upholding ethical research standards, advocating for open science, transparency, and reproducibility to foster trust and accountability (61).\u003c/p\u003e\n\u003ch3\u003eCluster 10: Mental health and illness\u003c/h3\u003e\n\u003cp\u003eThis cluster centers on terms associated with mental health and mental illness, with a focus on behavioral and motivational aspects, population-specific considerations, healthcare contexts, and implementation challenges. The connections between the terms suggest potential areas of discussion, including the role of exercise and motivation in behavior change, the mental health needs of children and adolescents, and barriers to implementation in primary healthcare and psychiatry.\u003c/p\u003e\n\u003cp\u003eExamples of literature representative of this cluster include a systematic review on the effects of motivational interviewing and wearable fitness trackers on physical activity (62); a study on the relationship between physical activity and adolescent mental wellbeing (63), and a systematic review aimed at understanding how psychiatry ethics differs from forensic psychiatry ethics (64).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eCluster 11: Ethics at the margins of life\u003c/h3\u003e\n\u003cp\u003eThis cluster relates to ethical dilemmas that arise at the boundaries of life and death, including issues such as cardiopulmonary resuscitation, euthanasia, and end-of-life care within the domains of bioethics and neuroethics. The inclusion of nursing highlights the role healthcare professionals play in the context of morally complex situations, where medical, ethical, and legal considerations intersect. Literature reviews within this cluster suggest a reflective and analytical approach to these debates, consolidating discussions on autonomy, dignity, and the ethical frameworks that guide decision-making in critical moments. Literature representative of this cluster includes a narrative review of the nursing ethics literature on euthanasia (65); a narrative review on the conditions defining moral distress (66); and ethical and logistical considerations on the use of extracorporeal cardiopulmonary resuscitation in relation to organ donation (67).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eCluster 12: Patient-centeredness, education, and innovation\u003c/h3\u003e\n\u003cp\u003eThis cluster focuses on the central role of patient-centered care and education in biomedical research and healthcare practices. In this cluster, adherence to ethical principles and human rights is highlighted as foundational to patient-centered approaches. Education is framed as a key enabler for addressing emerging ethical challenges, particularly in the context of big data and technological innovation. The cluster also explores how tools like biobanks and big data contribute to advancements in research, raising questions about the balance between innovation and patient-centered ethical practices. Literature representative of this cluster includes a systematic literature review on the ethical challenges in humanitarian surgery aimed at developing the foundation for an ethics curriculum for surgeons involved in humanitarian missions (68); a review on biobanking education, emphasizing the need for specialized training programs, updated university curricula, and international collaboration (69); and a study on the impact of Flipped Classroom and gamification on motivation, autonomy, and self-regulation in learning about healthy habits (70).\u003c/p\u003e\n\u003ch3\u003eCluster 13: Genome editing and CRISPR-Cas9\u003c/h3\u003e\n\u003cp\u003eThis small but significant cluster centers on genome editing technologies, particularly CRISPR/Cas9. It explores the ethical, regulatory, and societal implications of these technologies, highlighting the need for robust frameworks to guide their application in biomedical research and healthcare. Examples of literature belonging to this cluster include reflections on the transformative potential of CRISPR-Cas9 in genome editing while highlighting ethical, moral, and safety concerns, particularly regarding human germline modifications, calling for stringent regulations, global dialogue, and responsible governance (71); and on the potential of genome editing in agriculture to address climate change, sustainability, and food security while analyzing how regulatory uncertainty and consumer skepticism in the EU have slowed adoption, potentially impacting global acceptance and agricultural innovation (72).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study mapped the evolution of structured ethical considerations as they translate into policy-relevant discourse within biomedical and health-related literature. Results showed a consistent prominence and an increasing trend of terms relating to emerging phenomena, such as \u0026ldquo;artificial intelligence\u0026rdquo;; simultaneously, subtle declines in previously salient terms in this literature suggest a reframing of older debates within broader ethical conversations. In this sense, one thematic issue that stands out as receiving less attention is sustainability, which displays a small negative trend of -0.012 and a normalized frequency of 0.075. This is noteworthy as the concept of sustainability provides normative tools to response to urgent issues related to climate and environmental change. The reason could be that the impact of climate is not perceived as pressing, or that there is too little knowledge about the associations between climate changes and health challenges (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThematic clustering uncovered a rich ecology of topics, ranging from AI and crisis ethics to justice in global health, and from mobile health to genomic technologies. Notably, reviews and evidence synthesis emerged as methodological bridges between theory and implementation, signaling the emergence of an increasingly systematic ethics-to-policy interface.\u003c/p\u003e\u003cp\u003eBuilding on these results, we argue that a broader pattern emerges: the ethics-to-policy interface is not merely a straightforward, transactional shift of normative content from theory into practice. Instead, it represents a dialectical process in which ethical discourse itself evolves in response to pressures and affordances of policy contexts. Moreover, novel and emerging factors, such as AI, or public health crises, occur as drivers of the demand for ethical oversight. This aligns with the broader trajectory of policy discussions, where issues such as AI regulation and algorithmic bias (\u003cspan additionalcitationids=\"CR76 CR77\" citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e), or ethical infodemic management (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e) have emerged as pressing concerns, have been discussed in literature, and have resulted in translational guidance work. Let us zoom in on the dramatic emergence and rise of AI ethics as a paradigmatic case: this growth is not surprising, given the rapid deployment of AI systems across health, governance, and public communication. Generative AI models, predictive diagnostics, and decision-support algorithms have created new ethical terrains, redefining traditional concerns about bias, accountability, and trust (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), while posing novel questions around potential harm and epistemic opacity. As regulatory bodies attempt to catch up, the policy discourse has leaned on ethics to anticipate harm and safeguard rights (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e). This mirrors concerns raised in recent literature, where AI has been described both a risk vector and a useful tool (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeyond emerging trends, the presence of enduring ethical concepts (such as accountability in cluster 2; acceptability, beneficence and transparency in cluster 3; different models of justice in cluster 5; privacy, disclosure and confidentiality in cluster 8; self-determination theory in cluster 10; and patient-centered care and human rights in cluster 12) across clusters is not surprising. Rather than fading or being diluted by the emergence of new areas of inquiry, these concepts have become infrastructural: embedded in frameworks, protocols, and standards that guide translational governance. As discussed by Evans, bioethics has evolved from a deliberative discipline into a pragmatic orientation, capable of both normative critique and procedural articulation (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e). The persistence of these core ethics signals not necessarily stasis, but maturity: their incorporation into governance logics may mark a form of successful translation.\u003c/p\u003e\u003cp\u003eIn sum, our findings indicate that ethics has transitioned from being a peripheral issue to becoming increasingly integral and formative in policy agendas and governance frameworks (such as in the case of recent WHO guidance on infodemic management (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e), and UNESCO\u0026rsquo;s on AI ethics (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e)) \u0026ndash; and is continually reshaped by emerging policy demands driven by novel real-world challenges. This also supports the claim that translation in ethics involves distinguishable \u0026ldquo;movements of translation\u0026rdquo; or distinct \u0026ldquo;building blocks\u0026rdquo; of justifiable actions to bridge academic theory and practice effectively (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). It remains imperative for the academic community to engage in a sustained and focused reflection on the meta-ethical conditions required to ensure that the translation of research into practice is conducted justly and equitably.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWhat the overall picture suggests is that the horizon of ethics in translation is expanding. \u0026ldquo;Expansion\u0026rdquo; here does not mean proliferation alone, but also thickening: ethics is increasingly seen as not only a tool to resolve dilemmas, but to scaffold entire infrastructures of regulation, communication, and legitimacy of governance. This thickening is evident in the emerging positive trends of new concepts, in the structure of the clusters, in the presence of ethical concepts and the integration of ethical reflection into evidence-based methodologies, as well as in the integration of evidence-based methodologies into ethical reflection. In light of this, ethics-to-policy translation emerges as a field of iterative negotiation of how we ought to live together with our technologies, institutions and decisions.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eA limitation of this study is the restriction of our search to PubMed, which, while offering a structured and comprehensive repository for biomedical and health-related literature, may exclude relevant discussions from broader policy, legal, and social science domains. Although databases like Scopus or Web of Science could provide additional coverage, focusing on PubMed ensures methodological consistency, avoids redundancy, and aligns with the study\u0026rsquo;s emphasis on structured ethical work within healthcare and biomedical governance. Second, while the natural language processing tools employed in this study offer powerful capabilities for analyzing large volumes of text, it is important to acknowledge the limitations of the approach. Our analysis focuses on the normalized frequencies of keywords and on their co-occurrence patterns, which provides a high-level, broad mapping of the field but is not suited for fine-grained or nuanced assessments of the underlying content. This method allows us to identify trends, emerging areas of interest, shifts in focus, and thematic clusters across a vast body of literature, but it does not capture the depth or context of individual studies or the specific ways in which keywords are used. As a result, the findings presented here should be interpreted as a macro-level perspective, offering an overview of thematic patterns and trends rather than a detailed examination of specific topics. Drawing inferences beyond this high-level mapping, unless explicitly supported by literature or further analysis, would be unwarranted. The reliance on keyword frequencies means that subtle variations in the framing, context, or interpretation of concepts are likely to be missed, and complex interrelations between themes may not be fully captured.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eGS: Conceptualization; Methodology; Software; Data curation; Formal analysis; Visualization; Writing \u0026ndash; original draft; Project administration.FG: Methodology; Validation; Writing \u0026ndash; review \u0026amp; editing.KB: Conceptualization; Theoretical framework; Supervision; Writing \u0026ndash; review \u0026amp; editing.All authors have read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eGS wishes to thank Tiglia Panevinos, whose quiet presence and occasional commentary on fragmented governance structures reminded us that even in systems of disorder, there is elegance in observation. Her critical reflections helped sharpen our analysis of how ethical discourse interfaces with complex regulatory ecosystems.\u003c/p\u003e\u003ch2\u003eFunding details\u003c/h2\u003e\n\u003cp\u003eThis work was partly supported by a UZH GRC Travel Grant.\u003c/p\u003e\n\u003ch2\u003eDisclosure statement\u003c/h2\u003e\n\u003cp\u003eThe authors report there are no competing interests to declare.\u003c/p\u003e\n\u003ch2\u003eData availability statement\u003c/h2\u003e\n\u003cp\u003eThe data supporting the results and the code used for analyses presented in the paper can be found in this study\u0026rsquo;s OSF repository: https://osf.io/g68a5/\u003c/p\u003e\n\u003ch2\u003eEthics and Consent to Participate declarations\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBeauchamp TL, Childress JF. Principles of biomedical ethics. 7th ed. 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Oxford, New York: Oxford University Press; 2012. 256 p. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Translational ethics, ethics-to-policy, bioethics, ethical governance, policy translation, NLP, natural language processing, network analysis, evidence synthesis","lastPublishedDoi":"10.21203/rs.3.rs-6990403/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6990403/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe concept of \u0026ldquo;translation\u0026rdquo; is increasingly used to describe how ethical insights inform governance and policy-making. Yet, the diversity of translational pathways, the topics subject to translation, and the evolving role of ethics in the policy sphere remain underexplored. This study maps the biomedical literature on ethics-to-policy translation, analyzing 1,014 PubMed-indexed publications from 2014 to 2024. Using a natural language processing pipeline, we identify thematic trends and structural shifts in ethical governance. Our findings highlight a sharp rise in attention to artificial intelligence and digital technologies, with related terms showing the highest upward trends. At the same time, foundational ethical concepts such as autonomy, justice, and transparency, remain stable, underscoring their infrastructural role. We argue that translational ethics is expanding: broader in scope, more integrated across domains, and increasingly responsive to emergent regulatory challenges. 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